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This project is a Python implementation of Entity-based Topic Modeling, a new corpus exploration method. The tool identifies a series of descriptive labels for each document in an ontology and generates for each label a topic, which is easy to interpret as it is directly linked to a knowledge base.

The interconnected web-based evaluation platform for gathering annotations and statistics related to the results of the pipeline is available for download on Github via this link:

Installation instructions:

  • install Python 2.7
  • install Java 7 (that is required by the Stanford TMT!)
  • install Scala
  • install Python NLTK 3.2
  • run the following commands in the Python interpreter:

    import nltk'punkt')

  • install MySQL
  • install mysql-connector for Python
  • change configuration in settings file to your needs
  • run program

This project was part of the research that was done on Entity-based Topic Modeling by the Data and Web Science Research Group of the University of Mannheim. More information about our work can be found here:

Please do not forget to cite our work when using it in your project:

Anne Lauscher, Federico Nanni, Pablo Ruiz Fabo and Simone Paolo Ponzetto (2016): Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability. In: Italian Journal of Computational Linguistics 2(2), pp. 67-88.

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